1. Field
The present invention relates to detection of semiconductor fabrication defects, and in particular to a method and apparatus for automatic rule-based semiconductor fabrication defect signature recognition and defect sourcing.
2. Related Art
Conventional semiconductor fabrication systems incorporate clustering methods to group defects detected on a wafer surface that have commonality in position, size, or orientation into clusters. Drawbacks of this approach include: (a) clustering technique is primitive, and measurable attributes are not accurate enough to be used in a rule-based approach to identify defect patterns; (b) attributes extracted from clusters are insufficient for formulating a mathematical model for rule-based manipulation; and (c) successful defect sourcing depends on expert know-how that is difficult to capture.
Accordingly, there is need for (a) a mathematical model to capture the defect clusters, (b) automatic capture of measurable attributes like position, size, orientation, density and others, and (c) self-learning semiconductor fabrication defect signature recognition and sourcing algorithms using a rule-based approach for addressing the above problems.